AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 4881 to 4890 of 5128 articles

[An algorithm for three-dimensional plumonary parenchymal segmentation by integrating surfacelet transform with pulse coupled neural network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In order to overcome the difficulty in lung parenchymal segmentation due to the factors such as lung disease and bronchial interference, a segmentation algorithm for three-dimensional lung parenchymal is presented based on the integration of surfacel...

A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.

IEEE transactions on medical imaging
Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-C...

Comparison of data analytics strategies in computer vision systems to predict pig body composition traits from 3D images.

Journal of animal science
Computer vision systems (CVS) have been shown to be a powerful tool for the measurement of live pig body weight (BW) with no animal stress. With advances in precision farming, it is now possible to evaluate the growth performance of individual pigs m...

Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans.

IEEE transactions on medical imaging
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in ca...

A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.

IEEE transactions on medical imaging
Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to col...

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.

IEEE transactions on medical imaging
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid ...

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images.

IEEE transactions on medical imaging
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional...

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia.

IEEE transactions on medical imaging
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper h...

Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning.

IEEE transactions on medical imaging
Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the la...

Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images.

IEEE transactions on medical imaging
We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Inte...